The performance of data transaction scoring systems can often be improved by increasing the number of data feeds (i.e., data variables) that are provided to such systems. However, increasing the number of variables in a data scoring system can result in imprecise scores until there is a sufficient amount of exposure using such new variables. Depending on the size of data being characterized by the scoring system, the amount of time to conduct proper exposure using the new variables can be lengthy (which in turn can make such training costly). Moreover, unnecessary processing resources can be consumed while maturing the scoring system with the newly introduced variables.